code: def test_distributed_sampler_with_loop(self):
      batch_size = 16
      for length in [23, 64, 123]:
          dataset = list(range(length))
          shard1 = DistributedSamplerWithLoop(
              dataset, batch_size, num_replicas=2, rank=0
          )
          shard2 = DistributedSamplerWithLoop(
              dataset, batch_size, num_replicas=2, rank=1
          )
  
          # Set seeds
          shard1.set_epoch(0)
          shard2.set_epoch(0)
  
          # Sample
          samples1 = list(shard1)
          samples2 = list(shard2)
  
          self.assertTrue(len(samples1) % batch_size == 0)
          self.assertTrue(len(samples2) % batch_size == 0)
  
          total = []
          for sample1, sample2 in zip(samples1, samples2):
              total += [sample1, sample2]
  
          self.assertEqual(set(total[:length]), set(dataset))
          self.assertEqual(
              set(total[length:]), set(total[: (len(total) - length)])
          )
  
imports: import copy
  import unittest
  import numpy
  from transformers.data.data_collator import default_data_collator
  from transformers.testing_utils import require_accelerate, require_torch
  from transformers.trainer_utils import RemoveColumnsCollator, find_executable_batch_size
  from transformers.utils import is_torch_available
label_desc: Use image node_id 9 for calling the TrainerUtilsTest obj's underlying member method code with example usage: obj.test_distributed_sampler_with_loop() without return types